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      Characterization inference based on joint-optimization of multi-layer semantics and deep fusion matching network

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          Abstract

          The whole sentence representation reasoning process simultaneously comprises a sentence representation module and a semantic reasoning module. This paper combines the multi-layer semantic representation network with the deep fusion matching network to solve the limitations of only considering a sentence representation module or a reasoning model. It proposes a joint optimization method based on multi-layer semantics called the Semantic Fusion Deep Matching Network (SCF-DMN) to explore the influence of sentence representation and reasoning models on reasoning performance. Experiments on text entailment recognition tasks show that the joint optimization representation reasoning method performs better than the existing methods. The sentence representation optimization module and the improved optimization reasoning model can promote reasoning performance when used individually. However, the optimization of the reasoning model has a more significant impact on the final reasoning results. Furthermore, after comparing each module’s performance, there is a mutual constraint between the sentence representation module and the reasoning model. This condition restricts overall performance, resulting in no linear superposition of reasoning performance. Overall, by comparing the proposed methods with other existed methods that are tested using the same database, the proposed method solves the lack of in-depth interactive information and interpretability in the model design which would be inspirational for future improving and studying of natural language reasoning.

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          Glove: Global Vectors for Word Representation

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            A large annotated corpus for learning natural language inference

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              Hybrid speech recognition with Deep Bidirectional LSTM

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                Author and article information

                Contributors
                Journal
                PeerJ Comput Sci
                PeerJ Comput Sci
                peerj-cs
                PeerJ Computer Science
                PeerJ Inc. (San Diego, USA )
                2376-5992
                12 April 2022
                2022
                : 8
                : e908
                Affiliations
                [1 ]School of Automation, University of Electronic Science and Technology of China , Chengdu, China
                [2 ]Department of Geography and Anthropology, Louisiana State University and Agricultural and Mechanical College , Baton Rouge, Louisiana, United States
                Author information
                http://orcid.org/0000-0002-8486-1654
                Article
                cs-908
                10.7717/peerj-cs.908
                9044352
                37547057
                58fa2855-daf5-4117-8fea-22615d0933de
                © 2022 Zheng and Yin

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.

                History
                : 7 September 2021
                : 9 February 2022
                Funding
                Funded by: Sichuan Science and Technology Program
                Award ID: 2021YFQ0003
                This work was jointly supported by the Sichuan Science and Technology Program (No. 2021YFQ0003). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Artificial Intelligence
                Natural Language and Speech

                joint-optimization of multi-layer semantics,deep fusion matching network,meta-learning,characterization inference,natural language reasoning

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